Analysis of the Earthʼs magnetic field variations on the basis of a wavelet-based approach

In the present paper we will discuss a new wavelet-based approach aimed at processing and analyzing different features of complex geomagnetic signals. This approach makes it possible to automatically extract different kinds of disturbances in the Earth?s magnetic field variations, which characterize solar activity and help to predict magnetic storms. In order to analyze geomagnetic signals wavelet packets are used in order to isolate local variations for quiet and perturbed periods and determine their intensity. Furthermore, a new automatic method of calculating the index of geomagnetic activity K is suggested on the basis of forming a quiet-day diurnal variation (Sq-curve). This method allows us to do calculations in the way that is closest to that developed by J. Bartels, who introduced the K-index in 1938. The results are compared with those obtained by INTERMAGNET and the original method of J. Bartels and the advantages of the suggested method are clearly demonstrated. For geomagnetic data collected in high-latitude regions of our planet it has become possible to reduce the error of estimating the K-index by 20% and unlike the technique used by INTERMAGNET here all the calculations can be done automatically. We will use geomagnetic signals that were kindly provided to us by the Institute of Cosmophysical Research and Radio Wave Propagation (Paratunka, Kamchatka region, Far East of Russia) for the period from January, 2002 till December, 2010.     

New wavelet-based approach intended for the analysis of subtle features of complex natural signals

The present paper is devoted to the development of methods and algorithms intended for the analysis of complex natural signals (time series). Due to their variability, irregularity and complex structure the task of signal analysis and processing in the automatic mode is rather complicated. On the basis of contemporary methods of the analysis, processing, and recognition of complex data we have suggested a new approach, which allows us to automatically extract subtle features in complex natural signals of arbitrary structure. In addition, it becomes possible to identify components and characterize them in terms of a particular field. All the methods expounded in the following received approval from the Paratunka observatory (Paratunka, Kamchatka region, Far East Russia). The data were provided to the team of authors by the University of Cosmophysical Research and Radio Wave Propagation (Kamchatka region, Far East Russia).     

Reduction of Nonuniformity in the Thickness of a Galvanic Coating Using Disableable Anode Sections under Current Reversal Conditions

Theoretical Foundations of Chemical Engineering - A method of reducing the nonuniformity of a galvanic coating on the basis of disableable anode sections in the inverse current reversal regime was...     

Search for the Optimal Control over Current Regimes in Electroplating Processes with Multi Anodes at a Diversified Assortment of Treated Articles

Journal of Computer and Systems Sciences International - Methods of controlling electroplating processes are considered, and the shortcomings in the use of basic engineering and structural...     

Estimation of geomagnetic field disturbance using the wavelet transform

This paper discusses the main aspects of geomagnetic data processing using the wavelet transform. The wavelet transform is shown to be efficient for automatic extraction of unperturbed level of the horizontal component of the Earth’s magnetic field. As a result, it becomes possible to significantly reduce the errors arising during automatic calculations of the local geomagnetic activity index (local K-index) in comparison with adaptive smoothing (KAsm is Adaptative Smoothing method) recommended by INTERMAGNET. It has been found that prior to magnetic storms, we can observe a weak rise of geomagnetic activity in different frequency bands connected with the development of an approaching storm.     

Selecting a Single Result from an Aggregate of Contradictory Alternatives with Use of Multiset Theory

Journal of Computer and Systems Sciences International - We consider basic approaches to decision-making using computer systems. Ambiguous results in decision-making are obtained through the use of...     

Analyzing subtle features of natural time series by means of a wavelet-based approach

The present paper is devoted to the development of methods and approaches intended for the analysis of natural time series. Due to the strong variability, irregularity, and complex structure of the time series in question, the problem of automatic processing, i.e., in automatic mode, is rather complicated and merits further investigation in order to produce better solutions than those that presently exist. Relying on contemporary methods of signal processing, signal analysis, and recognition of complex data, we have suggested a new wavelet-based approach, which allows one to extract subtle structural features from a complex natural time series in an automatic mode. After that, it becomes possible to identify these features and analyze them in terms of a particular knowledge domain. Our methods and approaches have been successfully tested on the Earth??s magnetic field data obtained from the Paratunka observatory (Paratunka village, Kamchatka region, Far East of Russia).